The Application of GIS in the Interpretation of Radiometric and Electromagnetic Surveys to Mapping Geological and Environmental Features by Mark G. Stevenson, Performance and Initiatives Group, Bucks County Council, UK, Michael J. McCullagh, School of Geography, University of Nottingham, UK, and Rob Cuss, British Geological Survey, UK

Abstract This study looks at the application of GIS clustering approaches to airborne radiometric and electromagnetic point sampled data in an attempt to predict known geology in the UK at a small scale of part of the Dome area, and at a large scale of part of the Trent Valley. Spatially autocorrelated deviations in the automatic classification from the mapped geology were investigated further to determine likely causes for the misclassification. In the small scale trial the spatial resolution proved to be the critical limiting feature to accuracy and deviations were often related to analytical artefacts. In the large scale test spatial resolution once again was a problem, but the deviations provided some useful pointers to the possible development of an system. Introduction The aim of this study is to demonstrate the use of geographical information systems (GIS) in the context of geological and environmental mapping from aerial survey. Specific objectives are to investigate the usefulness of k-means clustering as a method of combining variables measured in airborne surveys (uranium, thorium and potassium) and then to correlate these results using GIS techniques in an attempt to identify the main geological units in the two study areas and target areas for further analysis. Particular questions to be answered are: S How well does the clustered data correlate to the mapped geology and is it possible to identify the main lithologies? S Is it possible to identify areas that deviate from the mapped geology, and establish possible causes for this difference? S Can environmental features (cultural noise) be identified from the main data set? The following discussion will demonstrate the advantage GIS has over conventional methods of geological analysis using the production of ternary plots to show the general radiometric pattern in an area. Airborne surveys were developed where the need for extensive geological mapping has meant traditional field surveys were not suitable, due to time and cost. This type of method is now being explored as a means of providing rapid geological and environmental information within the United Kingdom to update existing data sets and highlight areas that require further field investigation. The British Geological Survey (BGS) conducted two airborne geophysical surveys between 1998 and1999 over parts of central England. World Geoscience (UK) limited (now Fugro Airborne Surveys), conducted the first survey, High Resolution airborne Resource and Environmental Survey (Hi-Res-1). The survey area included the Cheshire Basin, Derbyshire Dome and the cities of Wrexham, Stoke on Trent, Nottingham and Lincoln. The second survey, a trial airborne geophysical survey was carried out by the Geological Survey of Finland (GTK) and was concerned with smaller site specific problems, relating to environmental features, concentrating on selected parts of the Trent Valley (gravel pits), Wolvey Villa Farm (landfill sites) and Langar (Kurimo 1999). This paper will investigate the airborne data for two contrasting regions in the main data set, the Derbyshire Dome and Trent Valley. These have been selected to demonstrate the way in which GIS could be used to correlate airborne data with existing geological data sets and help pull out environmental features. The Derbyshire Dome data is used as an example of attempting correlation of airborne data with the mapped geology; the Trent Valley study concentrates on environmental features. The equipment for the radiometry used sodium iodide (NaI) crystals; the electromagnetic survey used a dual frequency electromagnetic system (World Geoscience 1998).

2.0 Airborne geophysical surveys and geological mapping Figure 1: A 'typical' ternary diagram to show the distribution of uranium, Radiometric surveys attempt to establish the nature of the thorium and potassium in selected geology through the detection of naturally occurring rock types (Kearey and Brookes 1991). radioactive emanations called gamma-rays, (High-sense 2000). There are in excess of fifty radioactive isotopes that occur naturally. Only three of these isotopes emit gamma-rays of sufficient intensity to be of use during aerial surveying, potassium (40K), thorium (232Th) and uranium (238U), (Durrance 1987, IAEA 1991, Gupta 1991 and Campbell 1996). The abundance of these gamma-rays will depend on the geology. Limestone units are characterised by high values in uranium (figure 1), whereas shale and grit units are characterised by higher levels of thorium. As these isotopes become unstable, they spontaneously disintegrate to form other elements through their decay series (Durrance 1987). This disintegration is accompanied by the emission of radioactivity. Radiometric surveys record gamma-rays - the purest electromagnetic radiation released from the excited isotope during disintegration. There are several extraneous variables that can affect radiometric surveys, for example the interaction between the emitted isotopes and those produced by other processes, such as cosmic rays and during anthropogenic activity such as caesium (137Cs) release during the Chernobyl accident in 1988. The 137Cs isotopes were used to map the fall out from the Chernobyl accident (Burrough & McDonnell 1998, Sanderson & Ferguson 1997). Another problem is the Figure 2: Airborne surveys measure the relative amounts of uranium, attenuation of the gamma-ray thorium and potassium contained in the ground below. This can provide an signal by water. For this indication to the type of geological and environmental features present. reason surveying must not be carried out when it is extremely wet. Altitude is a more fundamental problem that can effect the outcome of a survey. During a survey, there are restrictions placed on the altitude when flying over urban areas (300 feet) and remote locations (130 feet). If the changes height suddenly, the radiometric equipment may not be able to recalibrate to the change in altitude, resulting in errors with the data (Kurimo 1999). A schematic of a typical field survey is given in Figure 2. The gamma-rays measured during AGRS surveys are defined by their energies, measured in electron volts (eV). During the acquisition and processing of data, a total count and selected peaks representing the three gamma-rays (Figure 3) are measured in the following energy intensities (Wilford et al., 1997): 40K indicator has an energy of 1.46 MeV, 238U indicator has an energy of 1.76 MeV, 232Th indicator has an energy of 2.62 MeV. By measuring gamma-ray emissions over different regions and comparing them, it should be possible Figure 3: Radiometric surveys produce measurement as a total count and peaks for uranium, thorium and potassium (after to translate changes in the energy Wilford et al., 1997) peaks into corresponding variations of uranium, thorium and potassium. These variations can then be used to establish relative lithologies and environmental features. Airborne electromagnetic surveys The second [Trent Valley] study area used AEM data as well as AGRS data. AEM surveys are widely used during geophysical investigations because of the speed of acquisition and thus cost, particularly in extensive areas (Kearey & Brookes 1991). There are two types of AEM surveys, passive and active. The GTK survey used an active system. The survey works by measuring the response of the ground to the propagation of a primary electromagnetic field produced by the transmitter. The primary field travels via a direct and indirect path to the receiver. Although the indirect field reaches the receiver with slightly reduced amplitude, its characteristics will be the same as the direct field (Reynolds 1997). If the ground is not homogeneous, the magnetic component of the primary field will induce alternating (eddy) currents. These eddies produce a secondary field, which travels to the receiver. The difference in amplitude and phase of the detected signal provides an indication on the geometry, size and electrical property of the conductive body (Milson 1987, Kearey & Brookes 1991 and Reynolds 1997). The frequency of the AEM survey dictates the depth to which the primary field will penetrate. High frequencies are used to investigate shallow geology as these frequencies are attenuated with depth, whereas lower frequencies are able to reach greater depths (Beamish et al., 2000). This study uses low frequency data in the Trent Valley study area in an attempt to penetrate the overlying drift geology. Conventional studies using airborne radiometric geophysical data The most common application of radiometric surveys has been in the exploration of economic ore bodies, particularly uranium, (Cook et al., 1996). For example in China, AGRS surveys account for a third of all uranium bodies located. The main task of these surveys has been to delimit potential areas of buried uranium, (Zhang et al., 1998). In the past, AGRS studies were considered 'specialised' and not directly applicable to geological mapping (Nrcan 2000b). However, since initial discussions on the application of AGRS surveys there has been extensive research on all continents except demonstrating the adaptation of AGRS to geological and environmental mapping (Darnley 1991). This is particularly important in remote areas where the extensive nature of the landscape makes field mapping difficult, for example in the outback of Australia or Canada. It also allows areas to be remapped in a faster and more cost efficient manner. Differences between the AGRS data and previously produced maps could be reinvestigated as these variations may indicate either incorrectly mapped units or subtle gradation in the known lithology or other geomorphological processes. One such study investigated the contact of sedimentary, magmatic and metamorphic rocks in order to estimate the age of the intrusive phase (Zhang 1998). The usefulness of these surveys to geological mapping does however hinge on the supposition that the variable distribution of gamma-rays relates to different lithologies. Through laboratory experiments, it has been established that lithologies vary in the amount of radio-elements they emit (Dickson & Scott 1997). For example, igneous rocks increase in the amount of thorium emitted with increasing levels of silica. It does not appear to be possible to devise a global classification system based on gamma-rays owing to the variability within the same rock types (Dickson & Scott 1997). This variation could lead to misclassification in gamma-ray surveys. An important point to consider when correlating radiometric surveys with bedrock is that this method will only reflect the surface geology. In an area where the bedrock is covered by drift (transported alluvium, lacustrine or marine silts/clays) this method will reflect the gamma-ray signature of the drift, rather than the underlying bedrock (Wilford et al, 1997). AGRS surveys only show the top 30-45 cm of the surface layer (AGSO 2000). Diagrammatic representation of radiometric data Many previous studies have presented the data in the form of ternary plots (Duval 1983). The data is gridded and presented using the three colour bands, red [K], green [Th] and blue [U]. The resultant colour scheme represents the intensity of gamma-rays at a particular point in the survey, based on the relationship shown in figure 1. One of the major problems with this method of interpretation is that subtle variations in the data may be overlooked. A conventional ternary plot for the study areas should show where a clear distinction between the main lithologies can be observed. However, although there are apparent subtle variations in the Trent valley area, the Derbyshire dome appears largely to be homogeneous. There have been a few studies looking at the interaction of regolith and soil and how they can affect AGRS surveys. Understanding this interaction is crucial in the interpretation of AGRS data, as it only measures the surface geology. The Figure 4: The model shows how areas of high erosion will produce a radiometric relationship between response reflecting the bedrock. Areas of stability (residual low) will produce a weathering, radiometric signature that is different to the bedrock as a result of the overlying regolith geomorphology and (Wilford et al., 1997). gamma-ray response has been modelled in figure 4 by Wilford (1997). Much of the work carried out into the effects of physical and chemical weathering has been in Australia, where there is a need for rapid, widespread geological survey. During the process of weathering, radioelements in the parent rock are released, redistributed and introduced into the regolith. Due to the effects of leaching and association with clay/iron oxides, the radiometric 'signature' of the regolith may differ from the parent rock. Where the bedrock appears as an outcrop, material is quickly removed leaving the soil/regolith thin. Gamma-ray response is therefore that of the solid geology. In areas of stability, resulting in soil/regolith being deposited, the gamma-ray response reflects that of the overlying drift. There are a growing number of studies showing the introduction of GIS methods into geology (Bonham Carter, 1994). Of particular interest for this paper is the one conducted by Graham and Bonham-Carter (1993) using an ternary plot constructed from uranium, thorium and potassium results. This showed a strong relationship between the gamma-ray data and the mapped lithology, although discrepancies did occur in some areas. From the ternary plots, the gamma-ray data was clustered and combined in a GIS with vector data showing the solid geology. A confusion matrix was then developed and a Kappa coefficient calculated to provide a measurement of correlation. The cross tabulation was able to identify those geological units with a distinctive radiometric response. Study areas and data sets The Derbyshire Dome study area is characterised by limestones, which can be sub- divided into three distinct units (Stevenson & Gaunt 1971 and Ford 1999): the Bee Low limestone (BLL), Cher Tor and the Millers Dale beds, which are indistinguishable, limestone, which is composed of a variety of sub units, and BLL (reef- apron), this has been sub-divided into the fore-reef, reef and back-reef making a reef complex. The back- reef passes into the standard limestone of the BLL. Figure 5 shows the limestone units of the Derbyshire dome surrounded by shales and grits. The limestone units have generally higher uranium value (Figure 1) than the surrounding shale and grit units as a result of organisms Figure 5: Limestone units of the Derbyshire dome surrounded by concentrating uranium. Although shales and grits. [Licence 2000/55 British Geological Survey. © NERC. All the shale and grit units are lower in rights reserved.] uranium values, an exception to this is the Edale shale group (ESH), which exhibit higher radioactivity in the upper sections close to the reef-apron. This increase in radioactivity has been linked to the presence of phosphatic fish and collophane, which both absorb uranium (Stevenson & Gaunt 1971). There are also several areas of mineralisation throughout the Derbyshire Dome, for example the caves and Treak cliff caverns. Mineralisation comes from deposits of , which have been mined extensively, resulting in open workings, now disused. The Derbyshire Dome contains minimal drift geology and cultural noise such as roads and water features. As AEM surveys are used to detect solid geology where the surface has been covered, only the AGRS survey data was used in the Dome. The second study area is the Trent Valley, which unlike the Derbyshire Dome is characterised by drift geology overlying the solid lithology (Figure 6). The drift is characterised by alluvium deposits in the main Valley. To the north-east and south- west of the study area, solid geology exists without drift covering. The area also contains cultural noise from several sources, such as fly ash filled sand and gravel pits, roads, railways and several connurbations. The Trent Valley study area is quite different to Figure 6: The geology of the Trent Valley study area, unlike the Derbyshire dome this area is characterised by drift geology. [Licence the Derbyshire Dome and owing to 2000/55 British Geological Survey. © NERC. All rights reserved.] the extensive drift geology both radiometric and electromagnetic surveys were employed in the hope of establishing a relationship between surface features and the sub-surface solid geology. Cluster Analysis Procedure This paper uses a (k-means) clustering program developed by the authors to cluster the data in a spatial context using the gamma-ray data in association with standardised geographical co- ordinates, to provide a spatial dimension to the analysis. On the basis of BGS advice the Uranium variable was given a higher weighting for the Variable Weight Derbyshire dome study, and Potassium a higher X & Y [Derbyshire Dome] 1 weighting for the Trent valley data set. Once X & Y [Trent Valley] 1 clustering was complete the relationship Uranium [Derbyshire Dome] 5 Uranium [Trent] 3 between the mapped geology and cluster Thorium [Derbyshire Dome] 3 membership could be assessed. This used either Thorium [Trent Valley] 5 the results at the raw data locations in the case Potassium [Derbyshire Dome] 3 of the Trent Valley area, or a nearest neighbour Potassium [Trent Valley] 10 surface interpolation of the results for the Table 1: Weightings of study variables Derbyshire Dome area to make it easier to see the clustering pattern. Once the initial pattern had been established it was then possible to determine areas of matched lithologies and clustering patterns and others where there appeared to be differences.

The Derbyshire Dome Analysis Figure 7 shows the main data set covering most of the north central area (see Figure 5) of the Derbyshire Dome, including the limestone and surrounding grit and shale units. It can be seen that clustering has been able to separate the limestone units from the shale and grit units based on their clustering classification. The outline of the Derbyshire Dome is characterised as predominantly high in uranium, low in thorium and potassium. The shale and grit units have broadly been classified as low in their radiometric response throughout the area. This differentiation can be related to figure 1, where the ternary plot classifies carbonates as having greater values of uranium when compared to silicates (shale and grits). Figure 8 contains only the northern section of the Dome area. In this sub-set, the clustering has been re-calculated so Figure 7: Correspondence between geology and there is a maximum between and radiometry, north central area of Derbyshire Dome. [Licence 2000/55 British Geological Survey. © NERC. All rights reserved.] minimum within class variance. Close inspection of the shale and grit units, shows the clustering does appear to have differentiated some of the units, for example the Chatsworth grit in larger geological units. In contrast the smaller data set, focussing on the limestone units in the Derbyshire Dome, shows a stronger differentiation of the geology, especially in separating the BLL, the reef-apron (RA) and the Monsal Dale limestone (MDL). Three distinctive areas in the western section of the Derbyshire Dome deviate from the ?? observed general pattern. These areas have been classified as ?? having very low ?? uranium, but high thorium and potassium which conflicts Figure 8: Three aberrant low uranium areas within the Derbyshire Dome. “??” bubbles with the rest of indicate the three suspect locations. [Licence 2000/55 British Geological Survey. © NERC. All rights reserved.] the classification for the geological unit. For example, the dolorite unit in the south of the Dome area is represented by one cluster, but the central dolorite unit (in the Dome area) by another. It can be seen from figure 9 that the location of these distinct cluster groups appears to be linked to quarries. Continuous video footage was recorded during the survey. Further investigation around the location of the dolorite unit containing the low radiometric values shows the quarries have been extended since 1995 base map in Figure 9 and cover most of the red ringed regions. Point in polygon analysis was Figure 9: Anomalous, very low uranium, clusters located in the eastern used to investigate areas where part of the Derbyshire dome, and associated with quarrying in the area. [Licence 2000/55 British Geological Survey. © NERC. All rights reserved. Topography the clustering results appeared to based on the 1995 Pathfinder Series map with permission of the Controller of Her strongly correlate or contradict Majesty’s Stationary Office. Crown copyright. Ordnance Survey licence number GD272191/2000]. the mapped geology. Table 2 shows the averages values and standard deviations for data points lying within the mapped geological units. Most units, particularly the Chatsworth grit (ChG) and the Reef Apron (RA), show low variability in their

% Uranium Thorium Potassium Geological Unit Homog. Average Std Dev Average Std Dev Average Std Dev BLL [Bee Low Limestone] 38 16 4 21 4 25 9 RA [Reef Apron] 61 51 12 19 8 13 17 ChG [Chatsworth Grit] 70 4 4 4 5 -4 11 ESH [Edale Shales] 34 20 5 20 6 20 12 D [Dolerite] 26 13 18 15 25 19 35 Table 2: Radiometric values grouped by location in specific geological units. Note the high variability for dolerite values resulting from quarry locations, and the strong differentiation between particular lithologies such as the reef apron and the Chatsworth Grit. cluster membership and high homogeneity. Homogeneity indicates the percentage of data locations within a mapped geological unit falling within a single cluster group. Both the RA and the ChG show a high concentration of a single cluster group within their mapped lithology, suggesting that clustering has been able to produce a strong signature for these lithologies. The converse of a strong signature within an area is that it should not occur outside the mapped unit. In practice only 13% of the signature group were found to fall within the ChG unit; the rest of the points are randomly distributed around the study area. This would suggest there are other, possibly random, variables affecting the results! The high variability of the dolorite category is expected as a result of the quarrying activity in the area as discussed previously and seen in Figures 8 and 9. Significance tests showed that all groups above were distinct at the 0.05 error level.

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, In contrast to the Derbyshire Dome study area, table 3 shows how 'radiometrically' similar the geology of the Trent Valley is. Any differences in the data set is therefore quite subtle to produce the relationship seen in figure 12. Inspection of figure 12 indicates that only the rivers and lakes have distinctive cluster membership; other features such as roads might be too subtle to be identified owing the relative sparcity of data collection. One area of particular geological interest is the alluvium deposit. Table 4 shows the radiometric signature is different in the Trent Valley compared with the surrounding river valleys. The table below shows the results of a point in polygon analysis on these two regions. The results suggest there is a difference in the radiometric signature

Figure 11: Clustering results from the AGRS data for the Trent Valley. There appears to be a good correlation between the mapped geology and the clusters. High radiometric values are in warm colours whilst low values Group B are in cold colours.[ Licence 2000/55 British Geological Survey. ©NERC. All rights reserved. Topography based on the 1995 Ordnance Survey 1:50,000 Landranger Series map and the 1995 1:25,000 Pathfinder Series map with the permission of the Controller of Her Majesty’s Stationary Office. Crown Copyright. Ordnance Survey licence Group A number GD272191/2000]. between the main river valley and the surrounding tributaries. Analysis was then carried out between the clustering data and environmental features. Particular interest in this study was placed on recognising areas containing spoil heaps and gravel pits. The aim was to see if a signature could be developed for these features which

Trent Cluster K U Th valley Average 4.4 14.6 18.1 2.8 Std dev 1.5 1.6 2.2 0.9 Other Cluster K U Th areas Average 6.7 10.3 18.2 2.6

STD Dev 2.6 1.4 2.0 0.9 Figure 12: The relationship between the cluster Table 4: Gamma-ray results gathered from the membership and cultural noise such as roads, alluvium deposits. The main radiometric difference is railways and conurbation. A different colour scheme found in the potassium count. was used to highlight the cultural features. [Licence 2000/55 British Geological Survey ©NERC All rights reserved] would identify the heap/pit data points in the main data set. This would allow environmental monitoring of spoil heaps and gravel pits to be carried out remotely using their radiometric response. Subtle variations in the data set, even in worked ground, made it difficult to isolate environmental features based on their radiometric signatures alone. Further analysis was carried out on other environmental features such as the lakes, rivers and roads. These are important features to identify, in studies where geology is the main focus, roads and water features would be considered noise and need removing. From an initial review of figure 11, only sections of the River Trent generate a distinctive signature. Figure 12 indicates the lakes have been classified into two groups. One group of lakes has a lower radiometric response (group A) than the second group (group B). The reasons for this are not clear, but may be due to sensor tracking problems related to altitude and time of survey, as all group A occur on different tracks to group B Conclusions The objective of this study was not to test a specific hypothesis, but rather demonstrate the capability of GIS in the interpretation of airborne gamma-ray survey (AGRS) and airborne electromagnetic (AEM) data for geological mapping and environmental monitoring. One of the points raised by Nrcan (2000) was that lithologies could be identified on the basis of their radiometric signature. This was crucial to the clustering process, whereby clustering was used to identify 'radiometric units' based on the AGRS data set. By using this technique it was hoped that, like Pires and Harthill (1989), clustering would enhance ternary diagrams such as those shown in figure 4. Through spatial manipulation GIS could then identify areas that either strongly correlated, or deviated, from the mapped geology. The results from the Derbyshire Dome study have broadly separated the region into limestone and shale & grit units. However, clustering does not appear to have distinguished the smaller sub units in the shale and grit units. For example in the northwest section of the study area, the geology is quite variable, and clustering has not been able to differentiate these smaller units. The culprit may be the 50m along path spatial resolution of the original survey. The effects of spatial resolution on the ability of clustering to differentiate geology units was further investigated by sub-dividing the data set into a smaller area that covered only the northern extent of the Derbyshire Dome. The sub set shown in Figure 8 indicates clearly the Dome has been generally divided into the following areas, based on cluster membership: S Bee Low limestone (BLL), Cher Tor (CT) where the cluster signature indicates low uranium, thorium and potassium. S Monsal Dale limestone (MDL), which is represented by high uranium, thorium and potassium. S Reef-apron (RA), which is very high in uranium, thorium and potassium. Despite clustering successfully separating the main limestone units in the Derbyshire Dome, analysis has shown differing degrees of homogeneity in the clustering signature for these units. For example, the BLL has an extremely high variation in its cluster signature, despite a similar response in the three radiometric elements. The dolorite has generally been classified as having low amounts of uranium, thorium and potassium. However, in the southern section of the Derbyshire Dome the classification indicates a very low radiometric response owing to the location of quarries in this area as shown in Figure 9. A significant relationship between elevation and the uranium count was discovered between the Bee low limestone the reef apron, and the Edale shales. Figure 10 shows the results from this, indicating a stepped increase in the uranium value towards the RA in the BLL, the ESH showing a more gradual change in its variation with altitude. The thorium and potassium values did not appear to follow this pattern. Variation in the BLL could be due to two factors, firstly gradation in the lithology towards the RA, or secondly it may be the effects of geomorphic processes. One essential point to note in figure 14 is the apparently low uranium value at the RA contact, before it increases again beyond one hundred metres. From reference to Figures 4 and 5 it is apparent that at least one lithology has been omitted from the geological map at the RA contact with the BLL. This unit forms the back-reef component of the reef complex, and is approximately fifty metres wide, although this distance is not accurate due to the sketch map itself being a generalisation. This may account for the dip in values. It is also possible that geomorphic process have been occurring in this area. The uranium value peaks at approximately the base of the slope leading from the RA (figure 17). The model produced by Wilford et al. (1997) suggests this area would be high in radiometric response as a result of erosional processes. This would indicate that erosion in the topographically higher RA may be depositing regolith into the lower ESH. The problem though is why deposition of the regolith is not occurring in the intermediate area, unless the back-reef is low in uranium resulting in the recorded value shown being higher than would be expected. This area would require further field analysis to determine the processes operating at this point. The main objective in the Trent Valley area was to complement the initial aims of the original GTK (Geological Survey of Finland) survey in the Trent Valley. The aim was to identify environmental features such as spoil heaps and landfill sites through their cluster and radiometric signature. This was for two reasons, firstly to develop a way of monitoring these sites for potential pollution hazards (Hollyer et al. 1996, Beamish et el. 2000) and secondly to find old landfill sites not previously identified. The differentiation of the main valley sediments from the smaller tributaries would indicate that despite the subtle difference in the radiometric values, clustering has been able to identify two sources for the alluvium (table 4). The main difference in the radiometric variation was shown to be in the potassium count, which might indicate the parent rock in the tributaries has a higher level of potassium than the source rock for the main valley sediments. However, Wilford et al. 1997 discussed how regolith would differ from the parent signal as a result of processes such as leaching acting on the sediments. Table 4 shows that the potassium count for the tributaries is lower than the Trent Valley. This may indicate that potassium is being leached from the sediments in the tributaries, more so than uranium and thorium. Another explanation is related to the course of the River Trent being altered by both ice and a fault. This change in direction could have resulted in the river passing through a geological region with a different radiometric signature to its previous course. This change in course may then be reflected in the different radiometric signature in the alluvium. The focus of the environmental analysis was concerned with the spoil heaps and gravel pits where the fill was composed partly of fly ash from local power stations. The process of monitoring these sites is reliant on there being a distinctive radiometric signature in these environmental features. Analysis suggests only the worked ground has a different clustering signature from the general trend. The results from the Derbyshire Dome, in respect to the quarries, showed how water could affect the radiometric response by attenuating the signal. For this reason, cultural “noise” needs to be identified including water bodies and roads. GIS was able to show how the clustering results highlighted a discrepancy in the lake classification, with group A being identified as having a low radiometric response, whilst group B had a higher response (Figures 11 & 12). This difference was supported by an analysis that showed group A's radiometric signal was lower than group B's. There are several possible explanations for this attenuation, firstly the geology may be different, however the lakes are found on the same geology. Secondly, water levels may be different, with higher levels in group B, resulting in the radiometric signal being attenuated. This is harder to investigate and would require field work to test the lake levels to see if any difference in the amount of water results in the radiometric response varying. The third explanation for this, as the difference in radiometric variation was not large, may be an artefact of the data collection process indicating the difference may be the result of error associated with the airborne survey.

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